NDLI: Spatiotemporal Localization and Categorization of Human Actions in Unsegmented Image Sequences

Content Provider	IEEE Xplore Digital Library
Author	Oikonomopoulos, A. Patras, I. Pantic, M.
Copyright Year	1992
Abstract	In this paper we address the problem of localization and recognition of human activities in unsegmented image sequences. The main contribution of the proposed method is the use of an implicit representation of the spatiotemporal shape of the activity which relies on the spatiotemporal localization of characteristic ensembles of feature descriptors. Evidence for the spatiotemporal localization of the activity is accumulated in a probabilistic spatiotemporal voting scheme. The local nature of the proposed voting framework allows us to deal with multiple activities taking place in the same scene, as well as with activities in the presence of clutter and occlusion. We use boosting in order to select characteristic ensembles per class. This leads to a set of class specific codebooks where each codeword is an ensemble of features. During training, we store the spatial positions of the codeword ensembles with respect to a set of reference points, as well as their temporal positions with respect to the start and end of the action instance. During testing, each activated codeword ensemble casts votes concerning the spatiotemporal position and extend of the action, using the information that was stored during training. Mean Shift mode estimation in the voting space provides the most probable hypotheses concerning the localization of the subjects at each frame, as well as the extend of the activities depicted in the image sequences. We present classification and localization results for a number of publicly available datasets, and for a number of sequences where there is a significant amount of clutter and occlusion.
Sponsorship	IEEE Signal Processing Society
Page Count	15
File Size	2215718
Starting Page	1126
Ending Page	1140
File Format	PDF
ISSN	10577149
Volume Number	20
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-04-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Spatiotemporal phenomena Feature extraction Humans Shape Training Clutter Image sequences space-time voting Action detection
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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